Computer Vision
Extracting meaning and building representations of visual objects and events in the world.
Our main research themes cover the areas of deep learning and artificial intelligence for object and action detection, classification and scene understanding, robotic vision and object manipulation, 3D processing and computational geometry, as well as simulation of physical systems to enhance machine learning systems.
Quick Links
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Researchers
Jeroen
van Baar
Tim
Marks
Michael
Jones
Anoop
Cherian
Alan
Sullivan
Matthew
Brand
Chiori
Hori
Ronald
Perry
Hassan
Mansour
Siheng
Chen
Takaaki
Hori
Jay
Thornton
Petros
Boufounos
Radu
Corcodel
Devesh
Jha
Daniel
Nikovski
Arvind
Raghunathan
Diego
Romeres
Anthony
Vetro
Dehong
Liu
Ye
Wang
Gordon
Wichern
William
Yerazunis
Bret
Harsham
Suhas
Lohit
Philip
Orlik
Huifang
Sun
Yebin
Wang
Siddarth
Jain
Takashi
Morimoto
Kuan-Chuan
Peng
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Awards
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AWARD MERL Researchers win Best Paper Award at ICCV 2019 Workshop on Statistical Deep Learning in Computer Vision Date: October 27, 2019
Awarded to: Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Chen Feng, Xiaoming Liu
MERL Contact: Tim Marks
Research Areas: Artificial Intelligence, Computer Vision, Machine LearningBrief- MERL researcher Tim Marks, former MERL interns Abhinav Kumar and Wenxuan Mou, and MERL consultants Professor Chen Feng (NYU) and Professor Xiaoming Liu (MSU) received the Best Oral Paper Award at the IEEE/CVF International Conference on Computer Vision (ICCV) 2019 Workshop on Statistical Deep Learning in Computer Vision (SDL-CV) held in Seoul, Korea. Their paper, entitled "UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss," describes a method which, given an image of a face, estimates not only the locations of facial landmarks but also the uncertainty of each landmark location estimate.
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AWARD R&D100 award for Deep Learning-based Water Detector Date: November 16, 2018
Awarded to: Ziming Zhang, Alan Sullivan, Hideaki Maehara, Kenji Taira, Kazuo Sugimoto
MERL Contact: Alan Sullivan
Research Areas: Artificial Intelligence, Computer Vision, Machine LearningBrief- Researchers and developers from MERL, Mitsubishi Electric and Mitsubishi Electric Engineering (MEE) have been recognized with an R&D100 award for the development of a deep learning-based water detector. Automatic detection of water levels in rivers and streams is critical for early warning of flash flooding. Existing systems require a height gauge be placed in the river or stream, something that is costly and sometimes impossible. The new deep learning-based water detector uses only images from a video camera along with 3D measurements of the river valley to determine water levels and warn of potential flooding. The system is robust to lighting and weather conditions working well during the night as well as during fog or rain. Deep learning is a relatively new technique that uses neural networks and AI that are trained from real data to perform human-level recognition tasks. This work is powered by Mitsubishi Electric's Maisart AI technology.
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AWARD CVPR 2011 Longuet-Higgins Prize Date: June 25, 2011
Awarded to: Paul A. Viola and Michael J. Jones
Awarded for: "Rapid Object Detection using a Boosted Cascade of Simple Features"
Awarded by: Conference on Computer Vision and Pattern Recognition (CVPR)
MERL Contact: Michael Jones
Research Area: Machine LearningBrief- Paper from 10 years ago with the largest impact on the field: "Rapid Object Detection using a Boosted Cascade of Simple Features", originally published at Conference on Computer Vision and Pattern Recognition (CVPR 2001)
See All Awards for MERL -
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News & Events
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NEWS MERL presenting 13 papers and an industry talk at ICASSP 2020 Date: May 4, 2020 - May 8, 2020
Where: Virtual Barcelona
MERL Contacts: Karl Berntorp; Petros Boufounos; Chiori Hori; Takaaki Hori; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Yanting Ma; Hassan Mansour; Niko Moritz; Philip Orlik; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern; Siheng Chen
Research Areas: Computational Sensing, Computer Vision, Machine Learning, Signal Processing, Speech & AudioBrief- MERL researchers are presenting 13 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held virtually from May 4-8, 2020. Petros Boufounos is also presenting a talk on the Computational Sensing Revolution in Array Processing (video) in ICASSP’s Industry Track, and Siheng Chen is co-organizing and chairing a special session on a Signal-Processing View of Graph Neural Networks.
Topics to be presented include recent advances in speech recognition, audio processing, scene understanding, computational sensing, array processing, and parameter estimation. Videos for all talks are available on MERL's YouTube channel, with corresponding links in the references below.
This year again, MERL is a sponsor of the conference and will be participating in the Student Job Fair; please join us to learn about our internship program and career opportunities.
ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year. Originally planned to be held in Barcelona, Spain, ICASSP has moved to a fully virtual setting due to the COVID-19 crisis, with free registration for participants not covering a paper.
- MERL researchers are presenting 13 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held virtually from May 4-8, 2020. Petros Boufounos is also presenting a talk on the Computational Sensing Revolution in Array Processing (video) in ICASSP’s Industry Track, and Siheng Chen is co-organizing and chairing a special session on a Signal-Processing View of Graph Neural Networks.
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NEWS MERL Scientists Presenting 11 Papers at IEEE Global Communications Conference (GLOBECOM) 2019 Date: December 9, 2019 - December 13, 2019
Where: Waikoloa, Hawaii, USA
MERL Contacts: Jianlin Guo; Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Rui Ma; Philip Orlik; Pu (Perry) Wang
Research Areas: Communications, Computer Vision, Machine Learning, Signal Processing, Information SecurityBrief- MERL Signal Processing scientists and collaborators will be presenting 11 papers at the IEEE Global Communications Conference (GLOBECOM) 2019, which is being held in Waikoloa, Hawaii from December 9-13, 2019. Topics to be presented include recent advances in power amplifier, MIMO algorithms, WiFi sensing, video casting, visible light communications, user authentication, vehicular communications, secrecy, and relay systems, including sophisticated machine learning applications. A number of these papers are a result of successful collaboration between MERL and world-leading Universities including: Osaka University, University of New South Wales, Oxford University, Princeton University, South China University of Technology, Massachusetts Institute of Technology and Aalborg University.
GLOBECOM is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 3000 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. Themed “Revolutionizing Communications,” GLOBECOM2019 will feature a comprehensive high-quality technical program including 13 symposia and a variety of tutorials and workshops to share visions and ideas, obtain updates on latest technologies and expand professional and social networking.
- MERL Signal Processing scientists and collaborators will be presenting 11 papers at the IEEE Global Communications Conference (GLOBECOM) 2019, which is being held in Waikoloa, Hawaii from December 9-13, 2019. Topics to be presented include recent advances in power amplifier, MIMO algorithms, WiFi sensing, video casting, visible light communications, user authentication, vehicular communications, secrecy, and relay systems, including sophisticated machine learning applications. A number of these papers are a result of successful collaboration between MERL and world-leading Universities including: Osaka University, University of New South Wales, Oxford University, Princeton University, South China University of Technology, Massachusetts Institute of Technology and Aalborg University.
See All News & Events for Computer Vision -
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Research Highlights
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Internships
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SP1419: Simulation of Multimodal Sensors
MERL is seeking a motivated intern to assist in generating simulated multimodal data for machine learning applications. The project involves integrating several existing software components to generate optical and radar data in a variety of sensing scenarios, and executing the simulations under a variety of conditions. The ideal candidate should have experience with C++, Python, and scripting methods. Some knowledge or experience with Blender, computer graphics, and computer vision would be preferred, but is not required. Project duration is flexible in the range of 1-2 months. Intern has the choice of part-time or full-time occupation and may start immediately.
See All Internships for Computer Vision -
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Openings
See All Openings at MERL -
Recent Publications
- , "3D Point Cloud Processing and Learning for Autonomous Driving", IEEE Signal Processing Magazine, May 2020.BibTeX TR2020-066 PDF
- @article{Chen2020may2,
- author = {Chen, Siheng and Liu, Baoan and Feng, Chen and Vallespi-Gonzalez, Carlos and Wellington, Carl},
- title = {3D Point Cloud Processing and Learning for Autonomous Driving},
- journal = {IEEE Signal Processing Magazine},
- year = 2020,
- month = may,
- url = {https://www.merl.com/publications/TR2020-066}
- }
- , "Tuning-Free Contact-Implicit Trajectory Optimization", IEEE International Conference on Robotics and Automation (ICRA), May 2020.BibTeX TR2020-065 PDF
- @inproceedings{Onol2020may,
- author = {Onol, Aykut O. and Corcodel, Radu and Long, Philip and Padir, Taskin},
- title = {Tuning-Free Contact-Implicit Trajectory Optimization},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2020,
- month = may,
- url = {https://www.merl.com/publications/TR2020-065}
- }
- , "Collaborative Localization Based on Traffic Landmarks for Autonomous Driving", IEEE International Symposium on Circuits and Systems (ISCAS), May 2020.BibTeX TR2020-064 PDF
- @inproceedings{Chen2020may,
- author = {Chen, Siheng and Zhang, Ningxiao and Sun, Huifang},
- title = {Collaborative Localization Based on Traffic Landmarks for Autonomous Driving},
- booktitle = {IEEE International Symposium on Circuits and Systems (ISCAS)},
- year = 2020,
- month = may,
- url = {https://www.merl.com/publications/TR2020-064}
- }
- , "Learning Plug-and-Play Proximal Quasi-Newton Denoisers", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP40776.2020.9054537, April 2020, pp. 8896-8900.BibTeX TR2020-045 PDF Video
- @inproceedings{Al-Shabili2020apr,
- author = {Al-Shabili, Abdullah and Mansour, Hassan and Boufounos, Petros T.},
- title = {Learning Plug-and-Play Proximal Quasi-Newton Denoisers},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2020,
- pages = {8896--8900},
- month = apr,
- publisher = {IEEE},
- doi = {10.1109/ICASSP40776.2020.9054537},
- issn = {2379-190X},
- isbn = {978-1-5090-6631-5},
- url = {https://www.merl.com/publications/TR2020-045}
- }
- , "Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP40776.2020.9053595, April 2020, pp. 4412-4416.BibTeX TR2020-046 PDF
- @inproceedings{Shi2020apr,
- author = {Shi, Lei and Geng, Shijie and Shuang, Kai and Hori, Chiori and Liu, Songxiang and Gao, Peng and Su, Sen},
- title = {Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2020,
- pages = {4412--4416},
- month = apr,
- publisher = {IEEE},
- doi = {10.1109/ICASSP40776.2020.9053595},
- issn = {2379-190X},
- isbn = {978-1-5090-6631-5},
- url = {https://www.merl.com/publications/TR2020-046}
- }
- , "Overview of the seventh Dialog System Technology Challenge: DSTC7", Computer Speech and Language, DOI: 10.1016/j.csl.2020.101068, Vol. 62, March 2020.BibTeX TR2020-029 PDF
- @article{D’Haro2020mar,
- author = {D’Haro, Luis, Fernando and Yoshino, Koichiro and Hori, Chiori and Marks, Tim and Polymenakos, Lazaros and Kummerfeld, Jonathan, k. and Galley, Michel and Gao, Xiang},
- title = {Overview of the seventh Dialog System Technology Challenge: DSTC7},
- journal = {Computer Speech and Language},
- year = 2020,
- volume = 62,
- month = mar,
- doi = {10.1016/j.csl.2020.101068},
- url = {https://www.merl.com/publications/TR2020-029}
- }
- , "Spatio-Temporal Ranked-Attention Networks for Video Captioning", IEEE Winter Conference on Applications of Computer Vision (WACV), February 2020.BibTeX TR2020-016 PDF
- @inproceedings{Cherian2020feb,
- author = {Cherian, Anoop and Wang, Jue and Hori, Chiori and Marks, Tim},
- title = {Spatio-Temporal Ranked-Attention Networks for Video Captioning},
- booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
- year = 2020,
- month = feb,
- url = {https://www.merl.com/publications/TR2020-016}
- }
- , "FX-GAN: Self-Supervised GAN Learning via Feature Exchange", IEEE Winter Conference on Applications of Computer Vision (WACV), February 2020, pp. 3194-3202.BibTeX TR2020-014 PDF
- @inproceedings{Huang2020feb,
- author = {Huang, Rui and Xu, Wenju and Lee, Teng-Yok and Cherian, Anoop and Wang, Ye and Marks, Tim},
- title = {FX-GAN: Self-Supervised GAN Learning via Feature Exchange},
- booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
- year = 2020,
- pages = {3194--3202},
- month = feb,
- url = {https://www.merl.com/publications/TR2020-014}
- }
- , "3D Point Cloud Processing and Learning for Autonomous Driving", IEEE Signal Processing Magazine, May 2020.
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Videos
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Calibration of Non-Overlapping Cameras Using an External SLAM System
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Gaussian Conditional Random Field Network for Semantic Segmentation
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Fast Plane Extraction in Organized Point Clouds Using Agglomerative Hierarchical Clustering
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Fast Graspability Evaluation on Single Depth Maps for Bin Picking with General Grippers
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Point-Plane SLAM for Hand-Held 3D Sensors
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FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds
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CASENet: Deep Category-Aware Semantic Edge Detection
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Pinpoint SLAM: A Hybrid of 2D and 3D Simultaneous Localization and Mapping for RGB-D Sensors
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MERL Research on Autonomous Vehicles
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Tracking an RGB-D Camera Using Points and Planes
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Voting-Based Pose Estimation for Robotic Assembly Using a 3D Sensor
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3D Reconstruction
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Learning to rank 3D features
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Fast Object Localization and Pose Estimation in Heavy Clutter for Robotic Bin Picking
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Deep Hierarchical Parsing for Semantic Segmentation
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Global Local Face Upsampling Network
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3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances
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Joint 3D Reconstruction of a Static Scene and Moving Objects
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Direct Multichannel Tracking
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Saffron - Digital Type System
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Obstacle Detection
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Semantic Scene Labeling
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Action Detection Using A Deep Recurrent Neural Network
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Robot Bin Picking
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Object Detection and Tracking in RGB-D SLAM via Hierarchical Feature Grouping
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Dose optimization for particle beam therapy
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Sapphire - High Accuracy NC Milling Simulation
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FasTFit: A fast T-spline fitting algorithm
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Software Downloads
